A truncated projected SVD method for linear discrete ill-posed problems

DOI10.1007/s11075-006-9053-3zbMath1114.65039OpenAlexW2088877504MaRDI QIDQ870762

Fiorella Sgallari, Serena Morigi, Lothar Reichel

Publication date: 15 March 2007

Published in: Numerical Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11075-006-9053-3

zbMATH Keywords

inverse problemdecompositionnumerical examplesSVDdiscrepancy principletruncated singular value decompositionQR factorizationTSVD

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22)

Related Items (16)

Parameter determination for Tikhonov regularization problems in general form ⋮ A special modified Tikhonov regularization matrix for discrete ill-posed problems ⋮ Subspace-restricted singular value decompositions for linear discrete ill-posed problems ⋮ Exploiting compression in solving discretized linear systems ⋮ An iterative method for Tikhonov regularization with a general linear regularization operator ⋮ Combining approximate solutions for linear discrete ill-posed problems ⋮ A projection method for general form linear least-squares problems ⋮ Optimization methods for regularization-based ill-posed problems: a survey and a multi-objective framework ⋮ A new Tikhonov regularization method ⋮ Tridiagonal Toeplitz matrices: properties and novel applications ⋮ Comparative studies on the criteria for regularization parameter selection based on moving force identification ⋮ Solving a class of nonlinear inverse problems using a feedback control approach ⋮ Simplified GSVD computations for the solution of linear discrete ill-posed problems ⋮ Inverse problems for regularization matrices ⋮ On the perturbation of an \(L^2\)-orthogonal projection ⋮ A modified Tikhonov regularization method

Uses Software

• Regularization tools

Cites Work

• Unnamed Item
• Partial least-squares vs. Lanczos bidiagonalization. I: Analysis of a projection method for multiple regression
• Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems
• GMRES, L-curves, and discrete ill-posed problems
• Regularization, GSVD and truncated GSVD
• Decomposition methods for large linear discrete ill-posed problems
• Truncated Singular Value Decomposition Solutions to Discrete Ill-Posed Problems with Ill-Determined Numerical Rank
• Computing the Generalized Singular Value Decomposition
• The Modified Truncated SVD Method for Regularization in General Form

This page was built for publication: A truncated projected SVD method for linear discrete ill-posed problems